A Fully Automated Breast Cancer Recognition System Using Discrete-Gradient Based Clustering and Multi Category Feature Selection

2005 ◽  
Vol 9 (3) ◽  
pp. 244-256
Author(s):  
Ranadhir Ghosh ◽  
◽  
Moumita Ghosh ◽  
John Yearwood
Keyword(s):  
Feature Selection ◽  
Diagnostic System ◽  
Recognition System ◽  
Machine Intelligence ◽  
Automated System ◽  
Cad System ◽  
Gradient Based ◽  
Feature Selection Approach ◽  
Mlp Model ◽  
Category Feature

Advances in machine intelligence have provided a whole new window of opportunities in medical research. Building a fully automated computer aided diagnostic system for digital mammograms is just one of them. Given some success with semi-automated systems earlier, a fully automated CAD system is just another step forward. A proper combination of a feature selection model and a classifier for those areas of a mammogram marked by radiologists has been very successful. However a fully automated system with only two modules is a time consuming process as the suspicious areas in a mammogram can be quite small when compared to the whole image. Thus an additional clustering process can help in reducing the time complexity of the overall process. In this paper we propose a fast clustering process to identify suspicious areas. Another novelty of this paper is a multi-category feature selection approach. The choice of features to represent the patterns affects several aspects of pattern recognition problems such as accuracy, required learning time and the required number of samples. In this paper we propose a hybrid canonical based feature extraction technique as a combination of an evolutionary algorithm based classifier with a feed forward MLP model.

A Multilevel Optimal Feature Selection and Ensemble Learning for a Specific CAD System-Pulmonary Nodule Detection

2013 ◽  
Vol 380-384 ◽  
pp. 1593-1599
Author(s):  
Hao Yan Guo ◽  
Da Zheng Wang
Keyword(s):  
Feature Selection ◽  
Learning Algorithm ◽  
Feature Selection Method ◽  
Feature Subset ◽  
Cad System ◽  
Optimal Feature Selection ◽  
Optimal Feature Subset ◽  
Feature Selection Approach ◽  
Selection Algorithms ◽  
Optimal Feature

The traditional motivation behind feature selection algorithms is to find the best subset of features for a task using one particular learning algorithm. However, it has been often found that no single classifier is entirely satisfactory for a particular task. Therefore, how to further improve the performance of these single systems on the basis of the previous optimal feature subset is a very important issue.We investigate the notion of optimal feature selection and present a practical feature selection approach that is based on an optimal feature subset of a single CAD system, which is referred to as a multilevel optimal feature selection method (MOFS) in this paper. Through MOFS, we select the different optimal feature subsets in order to eliminate features that are redundant or irrelevant and obtain optimal features.

scholarly journals A probabilistic segmentation and entropy-rank correlation-based feature selection approach for the recognition of fruit diseases

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Muhammad Attique Khan ◽  
Tallha Akram ◽  
Muhammad Sharif ◽  
Majed Alhaisoni ◽  
Tanzila Saba ◽  
...  
Keyword(s):  
Feature Selection ◽  
Critical Role ◽  
Rank Correlation ◽  
Automated System ◽  
Support Vector ◽  
Binary Images ◽  
Segmentation Approach ◽  
Correlation Based Feature Selection ◽  
Feature Selection Approach ◽  
Fusion Feature

AbstractAgriculture plays a critical role in the economy of several countries, by providing the main sources of income, employment, and food to their rural population. However, in recent years, it has been observed that plants and fruits are widely damaged by different diseases which cause a huge loss to the farmers, although this loss can be minimized by detecting plants’ diseases at their earlier stages using pattern recognition (PR) and machine learning (ML) techniques. In this article, an automated system is proposed for the identification and recognition of fruit diseases. Our approach is distinctive in a way, it overcomes the challenges like convex edges, inconsistency between colors, irregularity, visibility, scale, and origin. The proposed approach incorporates five primary steps including preprocessing,Standard instruction requires city and country for affiliations. Hence, please check if the provided information for each affiliation with missing data is correct and amend if deemed necessary. disease identification through segmentation, feature extraction and fusion, feature selection, and classification. The infection regions are extracted using the proposed adaptive and quartile deviation-based segmentation approach and fused resultant binary images by employing the weighted coefficient of correlation (CoC). Then the most appropriate features are selected using a novel framework of entropy and rank-based correlation (EaRbC). Finally, selected features are classified using multi-class support vector machine (MC-SCM). A PlantVillage dataset is utilized for the evaluation of the proposed system to achieving an average segmentation and classification accuracy of 93.74% and 97.7%, respectively. From the set of statistical measure, we sincerely believe that our proposed method outperforms existing method with greater accuracy.

Evaluation of Various Diagnostic Techniques for the Diagnosis of Pulmonary and Extra Pulmonary Tuberculosis at a Tertiary Care Center in North India

2020 ◽  
Vol 20 (4) ◽  
pp. 433-439
Author(s):  
Monika Rajani ◽  
Molay Banerjee
Keyword(s):  
Care Center ◽  
Tertiary Care ◽  
Diagnostic System ◽  
Diagnostic Techniques ◽  
Automated System ◽  
North India ◽  
Mortality And Morbidity ◽  
Indicator Tube ◽  
Extra Pulmonary ◽  
Uv Lamp

Introduction: Tuberculosis (TB) is a one of the main causes of mortality and morbidity worldwide. Bactec MGIT (Mycobacteria Growth Indicator Tube) system is a rapid, reliable automated system for early diagnosis of pulmonary and extra pulmonary TB in setups where purchase of expensive instruments is not possible. The present study was thus carried out to evaluate AFB microscopy, culture on Lowenstein Jensen media and micro MGIT system for early and accurate diagnosis of Tuberculosis. Methods: A total of 280 samples were processed for direct AFB smear examination, and culture on micro MGIT and LJ media. The identification of Mycobacterium tuberculosis complex in positive cultures was done by MPT64 Ag card test (BD MGIT TBC Identification Test). Results: Out of the processed samples, (47.1%) 132/280 were positive for Mycobacterium spp by Micro MGIT, (35%) 98/280 on LJ medium and (25.7%) 72/280 by AFB smear. A total of (48.5%) 136 samples were positive by a combination of Micro MGIT and LJ medium. Among the total positive samples (136/280), Micro MGIT was found to be positive in 97% (132/136) of samples, LJ was positive in 72% (98/136), while 52.9% (72/136) were positive by AFB smear. Conclusion: Manual MGIT System is a simple and efficient, safe to use the diagnostic system. It does not require any expensive/special instrumentation other than the UV lamp for the detection of fluorescence. In areas with limited resources where the purchase of expensive instruments such as the MGIT 960 is out of scope, the use of manual MGIT for rapid susceptibility testing for MDR-TB could be an option. We would recommend testing MGIT 960 using first and secondline drugs to determine DST.

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